Summary Cerebral malaria (CM) is a life-threatening clinical syndrome associated with malarial infection. Annually, malaria affects more than 200 million people and claims the lives of over 440,000 people worldwide, mostly African children. As a consequence of the high incidence of CM, it is often misdiagnosed for other pathologies with similar symptoms, leading to a high false positive rate for CM and incorrect treatment. An accurate means to confirm the presence of CM or to investigate for a non-malarial illness is critically needed to improve outcomes. Since Malarial retinopathy (MR) is greater than 90% specific and sensitive to the presence of CM once clinically diagnosed, retinal screening for MR represents an effective means to assist in and improve the specificity of CM diagnosis. VisionQuest Biomedical and its collaborators have assembled a team of inter-disciplinary scientists with considerable experience in automated retinal image analysis, clinical ophthalmology with specialized research in malarial retinopathy (MR), and cerebral malaria diagnosis (CM). This team will develop and test ASPIRE, a system for detection of MR consisting of automated MR detection software integrated with a low-cost and portable retinal camera. Our proposed ASPIRE system will augment, not replace, the current CM diagnostic standard; increasing the accuracy of CM diagnoses, leading to a smaller number of false positive outcomes. In Phases I and II, the research team at VisionQuest Biomedical developed the automated MR detection software and interfaced it with a handheld retinal camera. The resulting clinical prototype of ASPIRE was tested onsite in a health-clinic in Africa, which demonstrated excellent performance and usability for detecting MR, without the need of an ophthalmic expert. In Phase II-B, the MR detection system will be refined, productized, and the resulting commercial prototype will be validated on prospective datasets. We will accomplish this through three specific aims. In the first aim, the software system for MR detection will be adapted and improved to work with low-cost and portable iNview camera. In the second aim, we will refine iNview?s driver-software and integrate the camera with MR detection software to produce the first commercial prototype. The third aim will focus on collecting the retinal image data for algorithm testing as well as for validating the commercial prototype in an observational clinical study to be conducted at nine clinical sites in Malawi, Uganda, and Zambia in Africa.